Technological advances in the field of non-invasive imaging of the human body have produced gains in the ability of medical professionals to detect, diagnose, and treat diseases, thereby leading to improved outcomes and reduced morbidity and mortality. Advance in imaging resolution, functionality, and quality lead to new applications of the various imaging modalities for the benefit of patients and society in general. However, such advances incur human and financial costs, particularly in terms of the cost of imaging studies and the necessary professional medical expertise needed to utilize and interpret the resultant imagery. In the past decade, the rapid increase in three-and four-dimensional modalities (for example, computed tomography (CT), magnetic resonance imaging (MRI), single photon emission tomography (SPECT), and various others, along with rapid time sequences of such imaging operations) has led to an increase in the shear quantity of image data, and a concomitant increase in the demands and difficulty of physician interpretation of the imagery leading to subsequent diagnosis.
Improvements in technology often require parallel advances in multiple fields. State-of-the-art capabilities in automated computer image understanding are being applied to the problem of medical image understanding. Broadly, “image understanding” refers to the ability to create computer-based systems that mimic human capabilities to derive semantic-level (i.e., non-statistical and non-numeric) information from digitized images. Examples abound, and include such capabilities as face detection and recognition and scene classification. Much of current research related to automated understanding of medical images pertains to increased use of domain-expert knowledge (that is, anatomy and physiology) to raise the conceptual level of aids provided to the interpreters and other users of this imagery. Since the clinical practice of radiology faces a burgeoning information overload, hopes are being placed in the ability of advanced image understanding technology to aid the interpretive process. The TRIP initiative (Transforming the Radiological Interpretive Process) of the Society for Computer Applications in Radiology (SCAR) exists to foster research into assistive technologies that will boost capability, productivity, and accuracy in the extraction of diagnostic information from medical images, even in the face of proliferating data rates, types, and complexities.
Some believe that the practice of clinical radiology is approaching a “crisis” time in which the proliferating quantity and categories of medical imaging data are threatening to overwhelm the available professional interpretive skills necessary for its exploitation. The TRIP Initiative fosters innovative research to find promising technological and human-friendly advances to mitigate the inability to profitably handle overwhelming floods of imaging data. This initiative specifically addresses the potential of 3D image processing and the incorporation of expert medical knowledge into presentation systems as promising levers to raise the productivity and accuracy of radiological interpretation.
Among the tasks to which automated understanding of medical images might be applied, a need comprises the difficult task of disease detection in large data-volume medical images. There currently exist commercial and research-level computer-assisted detection (CAD) systems targeted to major diseases of organ systems, e.g., lung and colon cancer, operating with various medical imaging modalities, both two- and three-dimensional. A goal of such systems is to improve the accuracy and time efficiency of the medical professionals (usually radiologists) that interpret imaging studies. With respect to accuracy, the goal is to raise the rate of true detection of abnormal disease conditions, while not proliferating false findings. With respect to time efficiency, such systems serve to enable readers to evaluate studies more rapidly without loss of accuracy. This latter goal is particularly important as the data volumes involved in medical imaging studies increase rapidly as a result of increases in image resolution.
One impediment to the widespread acceptance of CAD systems concerns the legal and ethical responsibility enjoined upon the medical professional to discern incidental findings—that is, to detect abnormal pathologies that are outside the primary focus of the study. For example, a radiologist interpreting a Virtual Colonoscopy examination must also be alert for abnormal findings in the kidneys and liver as well. To date, CAD systems have been focused on the detection of disease in a single organ or organ system (e.g., the colon), requiring additional reader time to search for extra-colonic findings of medical significance. Thus, the ability for such CAD systems to substantially reduce interpretation time is greatly restricted.
Reference is made to the following references.
Commonly assigned US Patent Application Publication No. U.S. 2004/0024292 (Menhardt et al.), titled “System and Method for Assigning a Computer Aided Detection Application to a Digital Image”, directed to a system and method which assigns specific computer aided detection algorithms to digital medical images based upon knowledge of the exam type and the imaging modality.
Commonly assigned U.S. Patent Application Publication No. U.S. 2006/0110035 titled “Method For Classifying Radiographs”, filed on Nov. 23, 2004 to inventors Luo et al., directed to an exam type classification.
U.S. Patent Application Publication No. U.S. 2002/0164061A1, published on Nov. 7, 2002 to inventors Paik et al., titled “Method for Detecting Shapes in Medical Images”.
U.S. Patent Application Publication No. U.S. 2002/0164060A1, published on Nov. 7, 2002 to inventors Paik et al., titled “Method for Characterizing Shapes in Medical Images”.